Overview

Dataset statistics

Number of variables16
Number of observations137681
Missing cells2123
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.8 MiB
Average record size in memory128.0 B

Variable types

Numeric14
Unsupported2

Alerts

df_index is highly correlated with data_arc and 2 other fieldsHigh correlation
a is highly correlated with q and 3 other fieldsHigh correlation
e is highly correlated with q and 1 other fieldsHigh correlation
q is highly correlated with a and 4 other fieldsHigh correlation
ad is highly correlated with a and 3 other fieldsHigh correlation
per_y is highly correlated with a and 3 other fieldsHigh correlation
data_arc is highly correlated with df_index and 2 other fieldsHigh correlation
n_obs_used is highly correlated with df_index and 3 other fieldsHigh correlation
H is highly correlated with df_index and 2 other fieldsHigh correlation
albedo is highly correlated with n_obs_usedHigh correlation
moid is highly correlated with a and 4 other fieldsHigh correlation
df_index is highly correlated with data_arc and 2 other fieldsHigh correlation
a is highly correlated with ad and 1 other fieldsHigh correlation
e is highly correlated with qHigh correlation
q is highly correlated with e and 1 other fieldsHigh correlation
ad is highly correlated with a and 1 other fieldsHigh correlation
per_y is highly correlated with a and 1 other fieldsHigh correlation
data_arc is highly correlated with df_index and 2 other fieldsHigh correlation
n_obs_used is highly correlated with df_index and 2 other fieldsHigh correlation
H is highly correlated with df_index and 2 other fieldsHigh correlation
moid is highly correlated with qHigh correlation
df_index is highly correlated with data_arc and 2 other fieldsHigh correlation
a is highly correlated with q and 3 other fieldsHigh correlation
q is highly correlated with a and 2 other fieldsHigh correlation
ad is highly correlated with a and 1 other fieldsHigh correlation
per_y is highly correlated with a and 3 other fieldsHigh correlation
data_arc is highly correlated with df_index and 1 other fieldsHigh correlation
n_obs_used is highly correlated with df_index and 2 other fieldsHigh correlation
H is highly correlated with df_index and 1 other fieldsHigh correlation
moid is highly correlated with a and 2 other fieldsHigh correlation
df_index is highly correlated with data_arc and 2 other fieldsHigh correlation
a is highly correlated with e and 5 other fieldsHigh correlation
e is highly correlated with a and 2 other fieldsHigh correlation
i is highly correlated with a and 3 other fieldsHigh correlation
q is highly correlated with a and 1 other fieldsHigh correlation
ad is highly correlated with a and 3 other fieldsHigh correlation
per_y is highly correlated with a and 2 other fieldsHigh correlation
data_arc is highly correlated with df_index and 2 other fieldsHigh correlation
n_obs_used is highly correlated with df_index and 2 other fieldsHigh correlation
H is highly correlated with df_index and 2 other fieldsHigh correlation
moid is highly correlated with a and 1 other fieldsHigh correlation
a is highly skewed (γ1 = 179.5477471) Skewed
ad is highly skewed (γ1 = 203.526581) Skewed
per_y is highly skewed (γ1 = 254.3038078) Skewed
df_index has unique values Unique
a has unique values Unique
e has unique values Unique
i has unique values Unique
om has unique values Unique
w has unique values Unique
q has unique values Unique
ad has unique values Unique
per_y has unique values Unique
condition_code is an unsupported type, check if it needs cleaning or further analysis Unsupported
diameter is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-10-01 18:50:24.729849
Analysis finished2022-10-01 18:51:22.288179
Duration57.56 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct137681
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193659.6741
Minimum0
Maximum810411
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-10-02T00:21:22.424343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7664
Q149981
median131754
Q3269078
95-th percentile627536
Maximum810411
Range810411
Interquartile range (IQR)219097

Descriptive statistics

Standard deviation183351.5803
Coefficient of variation (CV)0.9467721203
Kurtosis0.5740239775
Mean193659.6741
Median Absolute Deviation (MAD)101005
Skewness1.186224009
Sum2.666325759 × 1010
Variance3.3617802 × 1010
MonotonicityStrictly increasing
2022-10-02T00:21:22.558526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
2325071
 
< 0.1%
2325491
 
< 0.1%
2325451
 
< 0.1%
2325331
 
< 0.1%
2325321
 
< 0.1%
2325311
 
< 0.1%
2325281
 
< 0.1%
2325251
 
< 0.1%
2325241
 
< 0.1%
Other values (137671)137671
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
8104111
< 0.1%
7997881
< 0.1%
7982251
< 0.1%
7981131
< 0.1%
7978961
< 0.1%
7978711
< 0.1%
7978411
< 0.1%
7974731
< 0.1%
7974451
< 0.1%
7974271
< 0.1%

a
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
UNIQUE

Distinct137681
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.814523873
Minimum0.6262255386
Maximum389.1459642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-10-02T00:21:22.698747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.6262255386
5-th percentile2.245546661
Q12.536646609
median2.750781171
Q33.09250633
95-th percentile3.217469342
Maximum389.1459642
Range388.5197387
Interquartile range (IQR)0.5558597209

Descriptive statistics

Standard deviation1.522511817
Coefficient of variation (CV)0.5409482692
Kurtosis40400.97362
Mean2.814523873
Median Absolute Deviation (MAD)0.3153860786
Skewness179.5477471
Sum387506.4613
Variance2.318042234
MonotonicityNot monotonic
2022-10-02T00:21:22.826703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.7691651551
 
< 0.1%
2.7817998531
 
< 0.1%
3.111997281
 
< 0.1%
3.0821995981
 
< 0.1%
2.7545087741
 
< 0.1%
2.7079001661
 
< 0.1%
3.1752691331
 
< 0.1%
2.6642850821
 
< 0.1%
2.7456051
 
< 0.1%
3.0582506241
 
< 0.1%
Other values (137671)137671
> 99.9%
ValueCountFrequency (%)
0.62622553861
< 0.1%
0.64219559631
< 0.1%
0.66183731071
< 0.1%
0.68226593771
< 0.1%
0.71206750971
< 0.1%
0.72004646021
< 0.1%
0.72838067241
< 0.1%
0.73873456951
< 0.1%
0.74667906211
< 0.1%
0.76158420711
< 0.1%
ValueCountFrequency (%)
389.14596421
< 0.1%
290.07923891
< 0.1%
167.16855211
< 0.1%
69.576833241
< 0.1%
66.839741391
< 0.1%
63.225317641
< 0.1%
53.920189941
< 0.1%
53.000048541
< 0.1%
42.781266791
< 0.1%
39.419335251
< 0.1%

e
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct137681
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.145477916
Minimum0.0004885363213
Maximum0.9843481641
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-10-02T00:21:22.963834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.0004885363213
5-th percentile0.03819892262
Q10.08961122155
median0.1385379705
Q30.1911336558
95-th percentile0.2723651364
Maximum0.9843481641
Range0.9838596277
Interquartile range (IQR)0.1015224342

Descriptive statistics

Standard deviation0.07757133208
Coefficient of variation (CV)0.5332172347
Kurtosis6.912563996
Mean0.145477916
Median Absolute Deviation (MAD)0.05066622246
Skewness1.395743109
Sum20029.54495
Variance0.006017311561
MonotonicityNot monotonic
2022-10-02T00:21:23.091666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07600902911
 
< 0.1%
0.12785414941
 
< 0.1%
0.17087868431
 
< 0.1%
0.065005894721
 
< 0.1%
0.19139788111
 
< 0.1%
0.059704050741
 
< 0.1%
0.18128252191
 
< 0.1%
0.10424535431
 
< 0.1%
0.07158308671
 
< 0.1%
0.075967200771
 
< 0.1%
Other values (137671)137671
> 99.9%
ValueCountFrequency (%)
0.00048853632131
< 0.1%
0.00060147181551
< 0.1%
0.00070799136161
< 0.1%
0.00088504304831
< 0.1%
0.00095167437831
< 0.1%
0.0010026455671
< 0.1%
0.0011407258611
< 0.1%
0.0012077527371
< 0.1%
0.001319960131
< 0.1%
0.0013875256991
< 0.1%
ValueCountFrequency (%)
0.98434816411
< 0.1%
0.96838106311
< 0.1%
0.9677729071
< 0.1%
0.96760432221
< 0.1%
0.96258321011
< 0.1%
0.95448028641
< 0.1%
0.94746359491
< 0.1%
0.94113769221
< 0.1%
0.92424210431
< 0.1%
0.91862962581
< 0.1%

i
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct137681
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.35085027
Minimum0.02185489629
Maximum170.3236472
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-10-02T00:21:23.220383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.02185489629
5-th percentile1.977917381
Q15.121634134
median9.391665752
Q313.74234679
95-th percentile24.77355997
Maximum170.3236472
Range170.3017923
Interquartile range (IQR)8.62071266

Descriptive statistics

Standard deviation6.835959366
Coefficient of variation (CV)0.660424911
Kurtosis13.04169278
Mean10.35085027
Median Absolute Deviation (MAD)4.302604195
Skewness1.635546681
Sum1425115.416
Variance46.73034046
MonotonicityNot monotonic
2022-10-02T00:21:23.340973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.594067041
 
< 0.1%
4.6807706251
 
< 0.1%
27.933690391
 
< 0.1%
9.2269902881
 
< 0.1%
4.6974899721
 
< 0.1%
6.6632484671
 
< 0.1%
9.1016976631
 
< 0.1%
14.172075681
 
< 0.1%
5.4639469351
 
< 0.1%
9.7251830791
 
< 0.1%
Other values (137671)137671
> 99.9%
ValueCountFrequency (%)
0.021854896291
< 0.1%
0.042710224411
< 0.1%
0.044738441131
< 0.1%
0.049260986641
< 0.1%
0.050460459741
< 0.1%
0.052952281891
< 0.1%
0.054974158061
< 0.1%
0.061629946351
< 0.1%
0.066551575121
< 0.1%
0.066711491511
< 0.1%
ValueCountFrequency (%)
170.32364721
< 0.1%
158.53539391
< 0.1%
151.8131361
< 0.1%
143.92019961
< 0.1%
143.91194561
< 0.1%
140.74906611
< 0.1%
129.24638781
< 0.1%
123.71051361
< 0.1%
121.38435511
< 0.1%
118.96996541
< 0.1%

om
Real number (ℝ≥0)

UNIQUE

Distinct137681
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.8289534
Minimum0.0007349159453
Maximum359.9908583
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-10-02T00:21:23.470625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.0007349159453
5-th percentile18.21759499
Q182.3300395
median160.4385012
Q3256.2797292
95-th percentile341.182864
Maximum359.9908583
Range359.9901234
Interquartile range (IQR)173.9496897

Descriptive statistics

Standard deviation102.7133349
Coefficient of variation (CV)0.6048046157
Kurtosis-1.133406742
Mean169.8289534
Median Absolute Deviation (MAD)85.31167554
Skewness0.1837598518
Sum23382220.14
Variance10550.02917
MonotonicityNot monotonic
2022-10-02T00:21:23.592683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.305531571
 
< 0.1%
188.26807811
 
< 0.1%
223.45252561
 
< 0.1%
328.67151851
 
< 0.1%
127.88487241
 
< 0.1%
282.45067041
 
< 0.1%
294.33322541
 
< 0.1%
346.88122771
 
< 0.1%
152.27128381
 
< 0.1%
356.3712011
 
< 0.1%
Other values (137671)137671
> 99.9%
ValueCountFrequency (%)
0.00073491594531
< 0.1%
0.00091659992081
< 0.1%
0.0012460824311
< 0.1%
0.0036702600411
< 0.1%
0.0038413412131
< 0.1%
0.0046656094321
< 0.1%
0.013691797211
< 0.1%
0.014966666081
< 0.1%
0.016909364441
< 0.1%
0.017481
< 0.1%
ValueCountFrequency (%)
359.99085831
< 0.1%
359.984051
< 0.1%
359.97566941
< 0.1%
359.96640381
< 0.1%
359.96465831
< 0.1%
359.9605591
< 0.1%
359.95229291
< 0.1%
359.94809621
< 0.1%
359.94491281
< 0.1%
359.93947341
< 0.1%

w
Real number (ℝ≥0)

UNIQUE

Distinct137681
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.8997547
Minimum0.004466267278
Maximum359.9951738
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-10-02T00:21:23.726771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.004466267278
5-th percentile18.85333456
Q191.9361685
median183.6624273
Q3271.7611751
95-th percentile341.839216
Maximum359.9951738
Range359.9907076
Interquartile range (IQR)179.8250066

Descriptive statistics

Standard deviation103.5575925
Coefficient of variation (CV)0.5693113369
Kurtosis-1.204809803
Mean181.8997547
Median Absolute Deviation (MAD)89.87311482
Skewness-0.02810874002
Sum25044140.13
Variance10724.17497
MonotonicityNot monotonic
2022-10-02T00:21:23.847490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73.597694121
 
< 0.1%
204.12938541
 
< 0.1%
168.22124531
 
< 0.1%
25.280821031
 
< 0.1%
225.28143491
 
< 0.1%
22.862206351
 
< 0.1%
63.666795711
 
< 0.1%
231.45674751
 
< 0.1%
225.88203441
 
< 0.1%
355.36543571
 
< 0.1%
Other values (137671)137671
> 99.9%
ValueCountFrequency (%)
0.0044662672781
< 0.1%
0.0058804501491
< 0.1%
0.01022452821
< 0.1%
0.011335745431
< 0.1%
0.011874025021
< 0.1%
0.012874316581
< 0.1%
0.012932513211
< 0.1%
0.015635792441
< 0.1%
0.016188254051
< 0.1%
0.017639905511
< 0.1%
ValueCountFrequency (%)
359.99517381
< 0.1%
359.99316251
< 0.1%
359.98888571
< 0.1%
359.98846181
< 0.1%
359.98526231
< 0.1%
359.98393841
< 0.1%
359.98079021
< 0.1%
359.97341631
< 0.1%
359.97304071
< 0.1%
359.97149421
< 0.1%

q
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct137681
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.402551476
Minimum0.08188215046
Maximum40.46567139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-10-02T00:21:23.978329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.08188215046
5-th percentile1.809358256
Q12.068878638
median2.363854066
Q32.685775789
95-th percentile2.995294493
Maximum40.46567139
Range40.38378924
Interquartile range (IQR)0.6168971512

Descriptive statistics

Standard deviation0.5161361597
Coefficient of variation (CV)0.214828346
Kurtosis345.6218442
Mean2.402551476
Median Absolute Deviation (MAD)0.3082834301
Skewness7.31135831
Sum330785.6898
Variance0.2663965354
MonotonicityNot monotonic
2022-10-02T00:21:24.104096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.55868361
 
< 0.1%
2.4261351991
 
< 0.1%
2.5802232791
 
< 0.1%
2.8818384551
 
< 0.1%
2.2273016311
 
< 0.1%
2.5462275571
 
< 0.1%
2.5996483371
 
< 0.1%
2.386545741
 
< 0.1%
2.5490661191
 
< 0.1%
2.8259238851
 
< 0.1%
Other values (137671)137671
> 99.9%
ValueCountFrequency (%)
0.081882150461
< 0.1%
0.092047538421
< 0.1%
0.095563049671
< 0.1%
0.11099850581
< 0.1%
0.13998111961
< 0.1%
0.1451538721
< 0.1%
0.18658824111
< 0.1%
0.19538602871
< 0.1%
0.19990249921
< 0.1%
0.19991407711
< 0.1%
ValueCountFrequency (%)
40.465671391
< 0.1%
32.032994961
< 0.1%
20.991864881
< 0.1%
18.746762591
< 0.1%
18.011118571
< 0.1%
17.579977541
< 0.1%
15.254182291
< 0.1%
14.948013431
< 0.1%
13.159583481
< 0.1%
13.13876651
< 0.1%

ad
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
UNIQUE

Distinct137681
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.226496269
Minimum0.999955915
Maximum772.2010796
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-10-02T00:21:24.240107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.999955915
5-th percentile2.523068374
Q12.864377243
median3.167359606
Q33.468509739
95-th percentile3.907310465
Maximum772.2010796
Range771.2011237
Interquartile range (IQR)0.6041324964

Descriptive statistics

Standard deviation2.895918499
Coefficient of variation (CV)0.8975428011
Kurtosis48076.96572
Mean3.226496269
Median Absolute Deviation (MAD)0.3022285751
Skewness203.526581
Sum444227.2328
Variance8.386343953
MonotonicityNot monotonic
2022-10-02T00:21:24.366188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.9796467091
 
< 0.1%
3.1374645081
 
< 0.1%
3.643771281
 
< 0.1%
3.282560741
 
< 0.1%
3.2817159171
 
< 0.1%
2.8695727751
 
< 0.1%
3.7508899291
 
< 0.1%
2.9420244241
 
< 0.1%
2.9421438811
 
< 0.1%
3.2905773641
 
< 0.1%
Other values (137671)137671
> 99.9%
ValueCountFrequency (%)
0.9999559151
< 0.1%
1.0145923121
< 0.1%
1.0156752131
< 0.1%
1.0186814721
< 0.1%
1.0201464861
< 0.1%
1.0276026511
< 0.1%
1.0310247971
< 0.1%
1.0355747591
< 0.1%
1.0381172691
< 0.1%
1.0387841241
< 0.1%
ValueCountFrequency (%)
772.20107961
< 0.1%
570.76116421
< 0.1%
328.94974761
< 0.1%
136.55032471
< 0.1%
122.72904721
< 0.1%
112.68761791
< 0.1%
105.38594831
< 0.1%
87.253334491
< 0.1%
59.814848141
< 0.1%
58.529066691
< 0.1%

per_y
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
UNIQUE

Distinct137681
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.883984786
Minimum0.4955692661
Maximum7676.742943
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-10-02T00:21:24.500359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.4955692661
5-th percentile3.365048501
Q14.040156183
median4.562388528
Q35.438436952
95-th percentile5.771382102
Maximum7676.742943
Range7676.247374
Interquartile range (IQR)1.398280769

Descriptive statistics

Standard deviation25.52840024
Coefficient of variation (CV)5.226961458
Kurtosis69768.00063
Mean4.883984786
Median Absolute Deviation (MAD)0.7900429081
Skewness254.3038078
Sum672431.9093
Variance651.6992186
MonotonicityNot monotonic
2022-10-02T00:21:24.628140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.6082018021
 
< 0.1%
4.6397760851
 
< 0.1%
5.4899326341
 
< 0.1%
5.4112717121
 
< 0.1%
4.5716654581
 
< 0.1%
4.4561230581
 
< 0.1%
5.6582093851
 
< 0.1%
4.3488982011
 
< 0.1%
4.5495169551
 
< 0.1%
5.3483252691
 
< 0.1%
Other values (137671)137671
> 99.9%
ValueCountFrequency (%)
0.49556926611
< 0.1%
0.51464669371
< 0.1%
0.53843721871
< 0.1%
0.56355815231
< 0.1%
0.60088305581
< 0.1%
0.61101093381
< 0.1%
0.62164983421
< 0.1%
0.63495191311
< 0.1%
0.64522199291
< 0.1%
0.66463789081
< 0.1%
ValueCountFrequency (%)
7676.7429431
< 0.1%
4940.6395791
< 0.1%
2161.4245171
< 0.1%
580.37031321
< 0.1%
546.46247351
< 0.1%
502.74149481
< 0.1%
395.94541871
< 0.1%
385.85364151
< 0.1%
279.82638541
< 0.1%
247.49825811
< 0.1%

data_arc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct16832
Distinct (%)12.2%
Missing140
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean8678.351023
Minimum1
Maximum25109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-10-02T00:21:24.762459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile169
Q16265
median7492
Q39655
95-th percentile22449
Maximum25109
Range25108
Interquartile range (IQR)3390

Descriptive statistics

Standard deviation5160.266163
Coefficient of variation (CV)0.5946136713
Kurtosis2.826156243
Mean8678.351023
Median Absolute Deviation (MAD)1624
Skewness1.527000718
Sum1193629078
Variance26628346.87
MonotonicityNot monotonic
2022-10-02T00:21:24.889409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14257
 
3.1%
251093440
 
2.5%
21808
 
1.3%
7150144
 
0.1%
5128
 
0.1%
4110
 
0.1%
7151103
 
0.1%
7175101
 
0.1%
7149100
 
0.1%
717995
 
0.1%
Other values (16822)127255
92.4%
(Missing)140
 
0.1%
ValueCountFrequency (%)
14257
3.1%
21808
1.3%
319
 
< 0.1%
4110
 
0.1%
5128
 
0.1%
644
 
< 0.1%
715
 
< 0.1%
86
 
< 0.1%
96
 
< 0.1%
1012
 
< 0.1%
ValueCountFrequency (%)
251093440
2.5%
251082
 
< 0.1%
251076
 
< 0.1%
251062
 
< 0.1%
251042
 
< 0.1%
251034
 
< 0.1%
251022
 
< 0.1%
250991
 
< 0.1%
250981
 
< 0.1%
250971
 
< 0.1%

condition_code
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size1.1 MiB

n_obs_used
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3079
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean659.4209513
Minimum5
Maximum9325
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-10-02T00:21:25.016584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile23
Q1214
median483
Q3958
95-th percentile1846
Maximum9325
Range9320
Interquartile range (IQR)744

Descriptive statistics

Standard deviation581.894272
Coefficient of variation (CV)0.8824321866
Kurtosis2.711255156
Mean659.4209513
Median Absolute Deviation (MAD)321
Skewness1.372468196
Sum90789736
Variance338600.9438
MonotonicityNot monotonic
2022-10-02T00:21:25.406217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13700
 
0.5%
11697
 
0.5%
12683
 
0.5%
10661
 
0.5%
14607
 
0.4%
9545
 
0.4%
15537
 
0.4%
8456
 
0.3%
16442
 
0.3%
17304
 
0.2%
Other values (3069)132049
95.9%
ValueCountFrequency (%)
582
 
0.1%
6165
 
0.1%
7268
 
0.2%
8456
0.3%
9545
0.4%
10661
0.5%
11697
0.5%
12683
0.5%
13700
0.5%
14607
0.4%
ValueCountFrequency (%)
93251
< 0.1%
84901
< 0.1%
81461
< 0.1%
71041
< 0.1%
66711
< 0.1%
61531
< 0.1%
61021
< 0.1%
60341
< 0.1%
57421
< 0.1%
54751
< 0.1%

H
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct337
Distinct (%)0.2%
Missing751
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean15.18753989
Minimum10.95449944
Maximum19.40014603
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-10-02T00:21:25.528104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10.95449944
5-th percentile12.8
Q114.4
median15.3
Q316.1
95-th percentile17.2
Maximum19.40014603
Range8.445646595
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.330430533
Coefficient of variation (CV)0.08760013423
Kurtosis0.8054683155
Mean15.18753989
Median Absolute Deviation (MAD)0.8
Skewness-0.467087434
Sum2079629.837
Variance1.770045403
MonotonicityNot monotonic
2022-10-02T00:21:25.638489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.24630
 
3.4%
15.44536
 
3.3%
15.54522
 
3.3%
15.34484
 
3.3%
15.14457
 
3.2%
15.74449
 
3.2%
15.64356
 
3.2%
14.94327
 
3.1%
154275
 
3.1%
15.94147
 
3.0%
Other values (327)92747
67.4%
ValueCountFrequency (%)
10.954499441449
1.1%
10.962
 
< 0.1%
10.973
 
< 0.1%
10.982
 
< 0.1%
10.992
 
< 0.1%
1182
 
0.1%
11.012
 
< 0.1%
11.032
 
< 0.1%
11.043
 
< 0.1%
11.051
 
< 0.1%
ValueCountFrequency (%)
19.40014603323
0.2%
19.421
 
< 0.1%
19.317
 
< 0.1%
19.217
 
< 0.1%
19.116
 
< 0.1%
19.031
 
< 0.1%
1913
 
< 0.1%
18.971
 
< 0.1%
18.951
 
< 0.1%
18.916
 
< 0.1%

diameter
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size1.1 MiB

albedo
Real number (ℝ≥0)

HIGH CORRELATION

Distinct628
Distinct (%)0.5%
Missing1232
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean0.1276118938
Minimum0.001
Maximum0.3905
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-10-02T00:21:25.758517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.034
Q10.053
median0.078
Q30.188
95-th percentile0.349
Maximum0.3905
Range0.3895
Interquartile range (IQR)0.135

Descriptive statistics

Standard deviation0.1013404292
Coefficient of variation (CV)0.7941299682
Kurtosis0.1581142022
Mean0.1276118938
Median Absolute Deviation (MAD)0.035
Skewness1.156554358
Sum17412.5153
Variance0.01026988259
MonotonicityNot monotonic
2022-10-02T00:21:25.882857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.39053809
 
2.8%
0.0571669
 
1.2%
0.0551655
 
1.2%
0.0531650
 
1.2%
0.0521636
 
1.2%
0.0491629
 
1.2%
0.0511628
 
1.2%
0.0561616
 
1.2%
0.0541606
 
1.2%
0.0481606
 
1.2%
Other values (618)117945
85.7%
ValueCountFrequency (%)
0.0015
< 0.1%
0.0042
 
< 0.1%
0.0053
 
< 0.1%
0.0064
< 0.1%
0.0073
 
< 0.1%
0.0084
< 0.1%
0.0094
< 0.1%
0.018
< 0.1%
0.0118
< 0.1%
0.01161
 
< 0.1%
ValueCountFrequency (%)
0.39053809
2.8%
0.3959
 
< 0.1%
0.38951
 
< 0.1%
0.38965
 
< 0.1%
0.38857
 
< 0.1%
0.38748
 
< 0.1%
0.38659
 
< 0.1%
0.38563
 
< 0.1%
0.38458
 
< 0.1%
0.38353
 
< 0.1%

moid
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct94849
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.420091357
Minimum0.000166392
Maximum39.507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-10-02T00:21:26.018596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.000166392
5-th percentile0.823873
Q11.08247
median1.3849
Q31.69933
95-th percentile2.01257
Maximum39.507
Range39.50683361
Interquartile range (IQR)0.61686

Descriptive statistics

Standard deviation0.5122072452
Coefficient of variation (CV)0.3606861226
Kurtosis357.6795121
Mean1.420091357
Median Absolute Deviation (MAD)0.30828
Skewness7.597426504
Sum195519.5981
Variance0.262356262
MonotonicityNot monotonic
2022-10-02T00:21:26.145946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.097019
 
< 0.1%
1.253518
 
< 0.1%
1.348558
 
< 0.1%
1.20478
 
< 0.1%
1.444478
 
< 0.1%
1.607438
 
< 0.1%
1.131667
 
< 0.1%
1.686167
 
< 0.1%
1.188087
 
< 0.1%
1.884297
 
< 0.1%
Other values (94839)137604
99.9%
ValueCountFrequency (%)
0.0001663921
< 0.1%
0.0003071031
< 0.1%
0.0003156831
< 0.1%
0.0003420791
< 0.1%
0.0004123691
< 0.1%
0.0004668021
< 0.1%
0.0005415111
< 0.1%
0.0006004081
< 0.1%
0.0006028571
< 0.1%
0.0007370311
< 0.1%
ValueCountFrequency (%)
39.5071
< 0.1%
31.01991
< 0.1%
20.11251
< 0.1%
18.2461
< 0.1%
17.00131
< 0.1%
16.59161
< 0.1%
14.27541
< 0.1%
13.98061
< 0.1%
12.21131
< 0.1%
12.18691
< 0.1%

Interactions

2022-10-02T00:21:18.546179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:41.157056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:46.093620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:50.269485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:52.651872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:54.943016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:57.142898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:59.328684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:01.666539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:03.892129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:06.174622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:08.563856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:11.650070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:15.852198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:18.704876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:41.343350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:46.636576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:50.534083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:52.815423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:55.100682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:57.302834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:59.486303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:01.834412image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:04.059049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:06.514897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:08.717473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:11.812576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:16.063942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:18.861890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:41.505539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:47.244129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:50.692350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:52.971779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:55.259553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:57.460099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:59.641026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:01.998406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:04.220814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:06.674770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:08.870503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:11.976287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:16.254847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:19.012672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:41.650258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:47.600299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:50.848398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:53.121914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:55.410462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:57.611576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:59.790296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:02.152646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:04.373222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:06.825586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:09.013779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:12.233970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:16.429124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:19.173343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:41.805329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:47.862768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:51.013124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:53.276819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:55.564550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:20:57.766045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:00.088202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:02.312004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:04.528627image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:06.982592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:09.164834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:12.493541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:16.595326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:21:19.330586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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Correlations

2022-10-02T00:21:26.270884image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-02T00:21:26.424681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-02T00:21:26.585185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-02T00:21:26.736921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-02T00:21:20.823531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-02T00:21:21.271582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-02T00:21:21.842988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-02T00:21:22.011098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexaeiomwqadper_ydata_arccondition_coden_obs_usedHdiameteralbedomoid
002.7691650.07600910.59406780.30553273.5976942.5586842.9796474.6082028822.00100210.954499939.40.09001.594780
112.7724660.23033734.836234173.080063310.0488572.1338653.4110674.61644425109.00849010.9544995450.10101.233240
222.6691500.25694212.988919169.852760248.1386261.9833323.3549674.36081425109.00710410.954499246.5960.21401.034540
332.3614180.0887217.141771103.810804150.7285412.1519092.5709263.62883724288.00932510.954499525.40.39051.139480
442.5742490.1910955.366988141.576604358.6876082.0823243.0661744.13032325109.00286110.954499106.6990.27401.095890
552.4251600.20300714.737901138.640203239.8074901.9328352.9174853.77675525109.00603410.954499185.180.26790.973965
662.3853340.2312065.523651259.563231145.2651061.8338312.9368373.68410525109.00520610.954499199.830.27660.846100
772.2017640.1564995.886955110.889330285.2874621.8571902.5463393.26711525109.00274410.954499147.4910.22600.874176
882.3856370.1231145.57681668.9085776.4173692.0919312.6793423.68480625109.00264910.9544991900.11801.106910
993.1415390.1124613.831560283.202167312.3152062.7882403.4948395.56829125109.00340910.954499407.120.07171.778390

Last rows

df_indexaeiomwqadper_ydata_arccondition_coden_obs_usedHdiameteralbedomoid
1376717974273.1959440.23294926.521186121.43996972.8490322.4514533.9404355.7135622278.027416.5000002.7540.1111.590970
1376727974453.1745050.21697227.544972282.826248339.0755632.4857273.8632835.6561675799.017515.3000004.390.0761.482280
1376737974733.1941570.5873506.487226232.63342368.1098311.3180685.0702455.7087702229.018919.4001461.8490.0430.324697
1376747978413.1589810.31558823.682254272.37173575.0708542.1620464.1559175.6147296294.009515.8000003.5060.0141.280800
1376757978713.1559750.43034928.718353115.532995136.8493981.7978054.5141455.6067162250.024718.2000001.0770.1160.854315
1376767978963.1712250.15911927.098625309.03657319.7468122.6666233.6758265.6474022373.015016.2000003.7930.0211.663010
1376777981132.5484100.07607111.593237246.298656170.0908102.3545492.7422704.0682913297.023317.3000002.6960.0611.367330
1376787982253.1462460.22055917.966646137.981403180.8988332.4523133.8401805.5808112839.014716.8000002.917NaN1.438370
1376797997883.0513360.28744914.456779343.917822342.6148392.1742313.9284405.3301962208.022717.2000003.2710.0721.166840
1376808104112.4174770.1090014.525668148.24481931.9498542.1539702.6809843.7588223458.032518.4000001.60.0231.159420